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Occupations that experience a decline in demand for their services are in areas such as printing, retailing, TCF, ground transport and banking staff. Printing and retailing were identified as losing industries reflecting e-commerce related savings in paper used (i.e. reduced use of hard copy) and savings of retail margins (that is, this stems from model assum<strong>pt</strong>ions). Employment in transport is reduced by e-commerce related labour-saving technical change. The TCF sector is harmed by the real appreciation associated with e-commerce related increases in real wage rates. It is expected that greater use of e-commerce for banking purposes will reduce the demand for teller staff. Regional impacts Given the expected industry impacts, because the location of industry in Australia is known, it is practicable to assess the geographical spread of the economic change in Australia. Impacts on State and Territory economies Exhibit 4.6 shows the deviation in gross state product (GSP) in Australia’s six States and two Territories. The results show that every State and Territory of Australia will obtain net benefits. This does not mean that sustained growth is projected over the timeframe to 2007 and <strong>beyond</strong>. That depends on trends that are forecast in the base case. What it does suggest is that all States and Territories will have a larger economy than they would otherwise have in the absence of e-commerce. Some, however, do better than others. Exhibit 4.6 Impact on the States 4 3.5 3 2.5 2 1.5 1 .5 0 Aust SA Vic Qld NSW WA TAS ACT NT 2007 2016 Source: MONASH model results. The State which is least stimulated is Western Australia. As noted earlier, some macro economic flow ons from the greater use of e-commerce disadvantages mining. The benefits expected to be obtained in Western Australia are offset somewhat by the reduction in mining in Western Australia. Queensland has a heavy concentration of mining and also agriculture. This explains its relatively low ranking in Exhibit 4.6. An offset for Queensland, which means that it performs better than Western Australia, is its well developed tourism industry. 30
Tasmania and the Northern Territory, like Queensland, have strong representations in their economies of commodity exports and of tourism. As a result these regions will obtain growth with greater use of e-commerce similar to that of Queensland. The industrial composition of the New South Wales economy is broadly in line with that of Australia at large. Thus the forecast deviation path for GSP in New South Wales is close to that for Australia’s GDP. Victoria and South Australia have above average prospects with greater use of e-commerce. Neither of these regional economies has a heavy reliance on commodity exporting. The Australian Capital Territory is the State/Territory with least reliance on commodity exports. It therefore obtains most of the benefits without significant costs. This gives it the top ranking in Exhibit 4.6. Statistical divisions The MONASH model also permits detailed disaggregation of simulation results into more detailed local impacts based on the Statistical Divisions used by the ABS. These divide Australia into 57 areas selected to reflect ‘identifiable social and economic links between inhabitants and between the economic units within the region, under the unifying influence of one or more major towns or cities’. 18 (See Appendix B about technical aspects of the MONASH model). The long run deviations from the base case forecasts for Statistical Divisions are shown in Table 4.2 below. This reveals that almost every region in Australia is better off as a result of the changes brought by greater use of e-commerce. Over 50 regions can expect a long run increase in the Gross Regional Product (GRP) of between one and four per cent. This is despite the fact that output in some sectors is expected to be lower as a result of the expected changes, particularly in retail, mining and agriculture. The biggest employment effect is an increase of 1.4 per cent and the smallest is a loss of 0.2 per cent. The main cause of variations in the results for Statistical Divisions is differences in dependence on tourism and dependence on commodity exporting. The top three Statistical Divisions shown in Table 4.2, Far North (QLD), Morton (QLD) and Kimberley (WA), all have heavy reliance on tourism. At the other end, we find Mackay (QLD), South East (SA), Barwon (VIC), Peel (WA), Far West (NSW), South West (WA), Fitzroy (QLD), North West (QLD) and Goldfields-Esperance (WA). All of these areas of Australia rely heavily on either export-oriented agriculture or export-oriented mining, activities that (in this simulation) are likely to obtain only modest additional direct gains from e-commerce. Further work to identify and analyse more of the direct gains that could be realised in mining and agriculture may find that the outcomes for these regions would be a net boost. Only one Statistical Division sees a decline in GRP and employment. This decline is small and little different from a zero change (i.e. the result is not larger than the rounding error involved in the shocks and model calculations). 18 ABS, Australian Standard Geographical Classification, Cat no. 1216.0, AGPS, Canberra, 1995, p. 18. 31
Page 2 and 3: © Commonwealth of Australia 2000 I
Page 5 and 6: Table of contents Foreword ........
Page 7 and 8: Part 2 - Case studies Section One I
Page 9 and 10: Executive summary E-commerce is sti
Page 11 and 12: Part 1— economic analysis
Page 13 and 14: Chapter One Study context, aims and
Page 15 and 16: Within the business-to-business cat
Page 17 and 18: Table 1.4 Projected growth in econo
Page 19 and 20: Chapter Two Economic issues What di
Page 21 and 22: Exhibit 2.1 Disintermediation effec
Page 23 and 24: Table 2.1 E-commerce impact on vari
Page 25 and 26: Chapter Three Changes ahead—indus
Page 27 and 28: Health Significant efficiencies sta
Page 29 and 30: Chapter Four Economy-wide assessmen
Page 31 and 32: Direct changes and flow on impacts
Page 33 and 34: consumption relative to economic ac
Page 35 and 36: External competitiveness Greater us
Page 37 and 38: Industry outcomes Greater use of e-
Page 39 and 40: Food products The food products sec
Page 41: people with differing occupational
Page 45 and 46: Table 4.2 Regional growth and emplo
Page 47: The data for country regions (Exhib
Page 50 and 51: impacts are a reasonable representa
Page 53 and 54: Chapter Six Policy implications and
Page 55 and 56: Appendix A Study methodology, shock
Page 57 and 58: confidence. This is at the expense
Page 59 and 60: All of the GDP gain generated by ch
Page 61 and 62: (s11) Saving of particular inputs I
Page 63 and 64: Table A.1 continued Commodity Per c
Page 65 and 66: Table A.2 continued Engine & Boiler
Page 67 and 68: Table A.2 continued Safety Inspecto
Page 69 and 70: Table A.3 continued Industries 2007
Page 71 and 72: B.2 The structure of MONASH The cor
Page 73 and 74: simulations it is assumed that the
Page 75 and 76: Table B.1 (Continued) MONASH-RR Reg
Page 77 and 78: C.2 Forecasts for macroeconomic var
Page 79 and 80: other grains, iron and non-iron ore
Page 81 and 82: The numbers in column (II) of Table
Page 83 and 84: Productivity growth over the histor
Page 85 and 86: forecast of a 0.1 per cent decline
Page 87 and 88: Productivity growth in the construc
Page 89 and 90: Employment prospects in the sector
Page 91 and 92: Table D.1 continued Textiles, cloth
Page 93 and 94:
Table D.1 continued Health, educati
Page 95 and 96:
Table D.2 continued 45 Bags and Con
Page 97:
Part 2 — case studies
Page 100 and 101:
Industry sectors’ use of e-commer
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e-commerce were chosen. Key questio
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Banking For some time now most of t
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2.3 Cost savings Banks are keen to
Page 108 and 109:
2.5 Changing business modes Branch
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Banks are also looking to take adva
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NAB, as a multinational, is already
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activities such as consumer electro
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Box 3.2 Building a business on e-co
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One of the issues for all industrie
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108
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Box 4.2 Australia Post and e-commer
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Opportunities are being created for
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Value can be transferred into Austr
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Continued use of email and the deve
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E-commerce allows some flexibility
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120
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are introduced to market at a start
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educe the duplication of assessment
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6.3 Cost savings as a result of ele
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The primary e-commerce tools for im
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New service modes and channels to t
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132
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This Section will review the implic
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of greater depth. (It should be not
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consumers watch five per cent less
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artist is the promotion which airpl
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New costs The fragmentation and reg
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gambled. The development of Interne
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146
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Making a purchase over the Internet
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Customers who access troubleshootin
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they does not actually sell those i
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Key points There are few other sect
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e-commerce allows quicker order pro
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9.3 New costs The demand for busine
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How a ticket is sold, through an ag
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Box 10.1 Case study: E-commerce and
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The education and training sector h
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166
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11.2 Production Manufacturing busin
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Opportunities for re-intermediation
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Value could transfer within the sec
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174
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years’ time. 30 This reflects an
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seeks to provide a point of referen
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To a small extent, direct selling t
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182
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Box 13.1 Austmine—a window for th
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Box 13.2 Leaving mining sites for w
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188
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